Data analysis is frequently used to discover useable information, provide suggestions or recommendations, and support decision making in a number of environments. For example, many businesses rely on data analysis to improve performance and quality. In any environment, correct data is critical to perform an accurate analysis of the data. In some cases, erroneous data can lead to false conclusions or misdirected guidance potentially resulting in costly consequences. In the process of data observations and data collections, however, incorrect data entries frequently occur during the observations and collections such that data is recorded in error. Some of the causes leading to erroneous data entries include, among others, erroneous data collection methods; constraints (e.g., time or physical) in collecting data; inability to repeat observations; human error, for instance, causing the destruction of data, incorrect measurements, and/or incorrect recordings; machine error such as distortion of data on memory; process error resulting from the storage, transformation, and/or handling of data.